Research

Real-time weapon detection in video is a challenging object detection task due to the small size of weapons relative to the image size. Thus, we try to solve the common problem that object detectors deteriorate dramatically as the object becomes smaller. In this manuscript, we aim to detect small-scale non-concealed rifles and handguns. Our contribution in this paper is (i) proposing a scale-invariant object detection framework that is particularly effective with small objects classification, (ii) designing anchor scales based on the effective receptive fields to extend the Single Shot Detection (SSD) model to take an input image of resolution 900*900, and (iii) proposing customized focal loss with hard-mining. Our proposed model achieved a recall rate of 86% (94% on rifles and 74% on handguns) with a false positive rate of 0.07% on a self-collected test set of 33K non-weapon images and 5K weapon images.